{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d988fcbb",
   "metadata": {},
   "source": [
    "# 基于MobileNetv2的垃圾分类"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "970120a2",
   "metadata": {},
   "source": [
    "本文档主要介绍垃圾分类代码开发的方法。通过读取本地图像数据作为输入，对图像中的垃圾物体进行检测，并且将检测结果图片保存到文件中。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e163f9e1",
   "metadata": {},
   "source": [
    "## 1、实验目的\n",
    "\n",
    "- 了解熟悉垃圾分类应用代码的编写（Python语言）；\n",
    "- 了解Linux操作系统的基本使用；\n",
    "- 掌握atc命令进行模型转换的基本操作。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41a6ae90",
   "metadata": {},
   "source": [
    "## 2、MobileNetv2模型原理介绍"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "9d77a45e",
   "metadata": {},
   "source": [
    "MobileNet网络是由Google团队于2017年提出的专注于移动端、嵌入式或IoT设备的轻量级CNN网络，相比于传统的卷积神经网络，MobileNet网络使用深度可分离卷积（Depthwise Separable Convolution）的思想在准确率小幅度降低的前提下，大大减小了模型参数与运算量。并引入宽度系数 α和分辨率系数 β使模型满足不同应用场景的需求。\n",
    " \n",
    "由于MobileNet网络中Relu激活函数处理低维特征信息时会存在大量的丢失，所以MobileNetV2网络提出使用倒残差结构（Inverted residual block）和Linear Bottlenecks来设计网络，以提高模型的准确率，且优化后的模型更小。\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "图中Inverted residual block结构是先使用1x1卷积进行升维，然后使用3x3的DepthWise卷积，最后使用1x1的卷积进行降维，与Residual block结构相反。Residual block是先使用1x1的卷积进行降维，然后使用3x3的卷积，最后使用1x1的卷积进行升维。\n",
    "- 说明：\n",
    "[详细内容可参见MobileNetV2论文](https://arxiv.org/pdf/1801.04381.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883ac391",
   "metadata": {},
   "source": [
    "## 3、实验环境\n",
    "\n",
    "本案例支持win_x86和Linux系统，CPU/GPU/Ascend均可运行。\n",
    "\n",
    "在动手进行实践之前，确保您已经正确安装了MindSpore。不同平台下的环境准备请参考《MindSpore环境搭建实验手册》。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bb79d4f",
   "metadata": {},
   "source": [
    "## 4、数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9505893b",
   "metadata": {},
   "source": [
    "### 4.1数据准备\n",
    "\n",
    "MobileNetV2的代码默认使用ImageFolder格式管理数据集，每一类图片整理成单独的一个文件夹， 数据集结构如下：\n",
    "\n",
    "└─ImageFolder\n",
    "\n",
    "    ├─train\n",
    "    │   class1Folder\n",
    "    │   ......\n",
    "    └─eval\n",
    "        class1Folder\n",
    "        ......\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4f978607013cd0d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%capture captured_output\n",
    "# 实验环境已经预装了mindspore==2.3.0，如需更换mindspore版本，可更改下面 MINDSPORE_VERSION 变量\n",
    "!pip uninstall mindspore -y\n",
    "!export MINDSPORE_VERSION=2.3.0\n",
    "!pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/${MINDSPORE_VERSION}/MindSpore/unified/aarch64/mindspore-${MINDSPORE_VERSION}-cp39-cp39-linux_aarch64.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.mirrors.ustc.edu.cn/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae031717184e022",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: mindspore\n",
      "Version: 2.3.0\n",
      "Summary: MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.\n",
      "Home-page: https://www.mindspore.cn\n",
      "Author: The MindSpore Authors\n",
      "Author-email: contact@mindspore.cn\n",
      "License: Apache 2.0\n",
      "Location: /home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages\n",
      "Requires: asttokens, astunparse, numpy, packaging, pillow, protobuf, psutil, scipy\n",
      "Required-by: mindnlp\n"
     ]
    }
   ],
   "source": [
    "# 查看当前 mindspore 版本\n",
    "!pip show mindspore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f72b86c4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://ascend-professional-construction-dataset.obs.cn-north-4.myhuaweicloud.com:443/MindStudio-pc/data_en.zip (21.3 MB)\n",
      "\n",
      "file_sizes: 100%|███████████████████████████| 22.4M/22.4M [00:00<00:00, 112MB/s]\n",
      "Extracting zip file...\n",
      "Successfully downloaded / unzipped to ./\n"
     ]
    }
   ],
   "source": [
    "from download import download\n",
    "\n",
    "# 下载data_en数据集\n",
    "url = \"https://ascend-professional-construction-dataset.obs.cn-north-4.myhuaweicloud.com:443/MindStudio-pc/data_en.zip\" \n",
    "path = download(url, \"./\", kind=\"zip\", replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1bed485",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://ascend-professional-construction-dataset.obs.cn-north-4.myhuaweicloud.com:443/ComputerVision/mobilenetV2-200_1067.zip (25.5 MB)\n",
      "\n",
      "file_sizes: 100%|███████████████████████████| 26.7M/26.7M [00:00<00:00, 118MB/s]\n",
      "Extracting zip file...\n",
      "Successfully downloaded / unzipped to ./\n"
     ]
    }
   ],
   "source": [
    "from download import download\n",
    "\n",
    "# 下载预训练权重文件\n",
    "url = \"https://ascend-professional-construction-dataset.obs.cn-north-4.myhuaweicloud.com:443/ComputerVision/mobilenetV2-200_1067.zip\" \n",
    "path = download(url, \"./\", kind=\"zip\", replace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08a605f1",
   "metadata": {},
   "source": [
    "### 4.2数据加载"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "136f52a2",
   "metadata": {},
   "source": [
    "###### 将模块导入，具体如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9cd16d34",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  setattr(self, word, getattr(machar, word).flat[0])\n",
      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  return self._float_to_str(self.smallest_subnormal)\n",
      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:549: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
      "  setattr(self, word, getattr(machar, word).flat[0])\n",
      "/home/mindspore/miniconda/envs/jupyter/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float32'> type is zero.\n",
      "  return self._float_to_str(self.smallest_subnormal)\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from easydict import EasyDict\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import mindspore.nn as nn\n",
    "from mindspore import ops as P\n",
    "from mindspore.ops import add\n",
    "from mindspore import Tensor\n",
    "import mindspore.common.dtype as mstype\n",
    "import mindspore.dataset as de\n",
    "import mindspore.dataset.vision as C\n",
    "import mindspore.dataset.transforms as C2\n",
    "import mindspore as ms\n",
    "from mindspore import set_context, nn, Tensor, load_checkpoint, save_checkpoint, export\n",
    "from mindspore.train import Model\n",
    "from mindspore.train import Callback, LossMonitor, ModelCheckpoint, CheckpointConfig\n",
    "\n",
    "os.environ['GLOG_v'] = '3' # Log level includes 3(ERROR), 2(WARNING), 1(INFO), 0(DEBUG).\n",
    "os.environ['GLOG_logtostderr'] = '0' # 0：输出到文件，1：输出到屏幕\n",
    "os.environ['GLOG_log_dir'] = '../../log' # 日志目录\n",
    "os.environ['GLOG_stderrthreshold'] = '2' # 输出到目录也输出到屏幕：3(ERROR), 2(WARNING), 1(INFO), 0(DEBUG).\n",
    "set_context(mode=ms.GRAPH_MODE, device_target=\"CPU\", device_id=0) # 设置采用图模式执行，设备为Ascend#"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55675c99",
   "metadata": {},
   "source": [
    "###### 配置后续训练、验证、推理用到的参数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2d5299f4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 垃圾分类数据集标签，以及用于标签映射的字典。\n",
    "garbage_classes = {\n",
    "    '干垃圾': ['贝壳', '打火机', '旧镜子', '扫把', '陶瓷碗', '牙刷', '一次性筷子', '脏污衣服'],\n",
    "    '可回收物': ['报纸', '玻璃制品', '篮球', '塑料瓶', '硬纸板', '玻璃瓶', '金属制品', '帽子', '易拉罐', '纸张'],\n",
    "    '湿垃圾': ['菜叶', '橙皮', '蛋壳', '香蕉皮'],\n",
    "    '有害垃圾': ['电池', '药片胶囊', '荧光灯', '油漆桶']\n",
    "}\n",
    "\n",
    "class_cn = ['贝壳', '打火机', '旧镜子', '扫把', '陶瓷碗', '牙刷', '一次性筷子', '脏污衣服',\n",
    "            '报纸', '玻璃制品', '篮球', '塑料瓶', '硬纸板', '玻璃瓶', '金属制品', '帽子', '易拉罐', '纸张',\n",
    "            '菜叶', '橙皮', '蛋壳', '香蕉皮',\n",
    "            '电池', '药片胶囊', '荧光灯', '油漆桶']\n",
    "class_en = ['Seashell', 'Lighter','Old Mirror', 'Broom','Ceramic Bowl', 'Toothbrush','Disposable Chopsticks','Dirty Cloth',\n",
    "            'Newspaper', 'Glassware', 'Basketball', 'Plastic Bottle', 'Cardboard','Glass Bottle', 'Metalware', 'Hats', 'Cans', 'Paper',\n",
    "            'Vegetable Leaf','Orange Peel', 'Eggshell','Banana Peel',\n",
    "            'Battery', 'Tablet capsules','Fluorescent lamp', 'Paint bucket']\n",
    "\n",
    "index_en = {'Seashell': 0, 'Lighter': 1, 'Old Mirror': 2, 'Broom': 3, 'Ceramic Bowl': 4, 'Toothbrush': 5, 'Disposable Chopsticks': 6, 'Dirty Cloth': 7,\n",
    "            'Newspaper': 8, 'Glassware': 9, 'Basketball': 10, 'Plastic Bottle': 11, 'Cardboard': 12, 'Glass Bottle': 13, 'Metalware': 14, 'Hats': 15, 'Cans': 16, 'Paper': 17,\n",
    "            'Vegetable Leaf': 18, 'Orange Peel': 19, 'Eggshell': 20, 'Banana Peel': 21,\n",
    "            'Battery': 22, 'Tablet capsules': 23, 'Fluorescent lamp': 24, 'Paint bucket': 25}\n",
    "\n",
    "# 训练超参\n",
    "config = EasyDict({\n",
    "    \"num_classes\": 26,\n",
    "    \"image_height\": 224,\n",
    "    \"image_width\": 224,\n",
    "    #\"data_split\": [0.9, 0.1],\n",
    "    \"backbone_out_channels\":1280,\n",
    "    \"batch_size\": 16,\n",
    "    \"eval_batch_size\": 8,\n",
    "    \"epochs\": 10,\n",
    "    \"lr_max\": 0.05,\n",
    "    \"momentum\": 0.9,\n",
    "    \"weight_decay\": 1e-4,\n",
    "    \"save_ckpt_epochs\": 1,\n",
    "    \"dataset_path\": \"./data_en\",\n",
    "    \"class_index\": index_en,\n",
    "    \"pretrained_ckpt\": \"./mobilenetV2-200_1067.ckpt\" # mobilenetV2-200_1067.ckpt \n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c28af32",
   "metadata": {},
   "source": [
    "###### 数据预处理操作\n",
    "\n",
    "利用ImageFolderDataset方法读取垃圾分类数据集，并整体对数据集进行处理。\n",
    "\n",
    "读取数据集时指定训练集和测试集，首先对整个数据集进行归一化，修改图像频道等预处理操作。然后对训练集的数据依次进行RandomCropDecodeResize、RandomHorizontalFlip、RandomColorAdjust、shuffle操作，以增加训练数据的丰富度；对测试集进行Decode、Resize、CenterCrop等预处理操作；最后返回处理后的数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f22eb88c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_dataset(dataset_path, config, training=True, buffer_size=1000):\n",
    "    \"\"\"\n",
    "    create a train or eval dataset\n",
    "\n",
    "    Args:\n",
    "        dataset_path(string): the path of dataset.\n",
    "        config(struct): the config of train and eval in diffirent platform.\n",
    "\n",
    "    Returns:\n",
    "        train_dataset, val_dataset\n",
    "    \"\"\"\n",
    "    data_path = os.path.join(dataset_path, 'train' if training else 'test')\n",
    "    ds = de.ImageFolderDataset(data_path, num_parallel_workers=4, class_indexing=config.class_index)\n",
    "    resize_height = config.image_height\n",
    "    resize_width = config.image_width\n",
    "    \n",
    "    normalize_op = C.Normalize(mean=[0.485*255, 0.456*255, 0.406*255], std=[0.229*255, 0.224*255, 0.225*255])\n",
    "    change_swap_op = C.HWC2CHW()\n",
    "    type_cast_op = C2.TypeCast(mstype.int32)\n",
    "\n",
    "    if training:\n",
    "        crop_decode_resize = C.RandomCropDecodeResize(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))\n",
    "        horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5)\n",
    "        color_adjust = C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)\n",
    "    \n",
    "        train_trans = [crop_decode_resize, horizontal_flip_op, color_adjust, normalize_op, change_swap_op]\n",
    "        train_ds = ds.map(input_columns=\"image\", operations=train_trans, num_parallel_workers=4)\n",
    "        train_ds = train_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=4)\n",
    "        \n",
    "        train_ds = train_ds.shuffle(buffer_size=buffer_size)\n",
    "        ds = train_ds.batch(config.batch_size, drop_remainder=True)\n",
    "    else:\n",
    "        decode_op = C.Decode()\n",
    "        resize_op = C.Resize((int(resize_width/0.875), int(resize_width/0.875)))\n",
    "        center_crop = C.CenterCrop(resize_width)\n",
    "        \n",
    "        eval_trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]\n",
    "        eval_ds = ds.map(input_columns=\"image\", operations=eval_trans, num_parallel_workers=4)\n",
    "        eval_ds = eval_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=4)\n",
    "        ds = eval_ds.batch(config.eval_batch_size, drop_remainder=True)\n",
    "\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "010b12e8",
   "metadata": {},
   "source": [
    "###### 展示部分处理后的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c122b5f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.8044444..2.64].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-2.117904..2.64].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.2467102..2.4831374].\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.8606442..2.64].\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = create_dataset(dataset_path=config.dataset_path, config=config, training=False)\n",
    "print(ds.get_dataset_size())\n",
    "data = ds.create_dict_iterator(output_numpy=True)._get_next()\n",
    "images = data['image']\n",
    "labels = data['label']\n",
    "\n",
    "for i in range(1, 5):\n",
    "    plt.subplot(2, 2, i)\n",
    "    plt.imshow(np.transpose(images[i], (1,2,0)))\n",
    "    plt.title('label: %s' % class_en[labels[i]])\n",
    "    plt.xticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9adf4bf4",
   "metadata": {},
   "source": [
    "## 5、MobileNetV2模型搭建"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f47985f",
   "metadata": {},
   "source": [
    "使用MindSpore定义MobileNetV2网络的各模块时需要继承mindspore.nn.Cell。Cell是所有神经网络（Conv2d等）的基类。\n",
    "\n",
    "神经网络的各层需要预先在__init__方法中定义，然后通过定义construct方法来完成神经网络的前向构造。原始模型激活函数为ReLU6，池化模块采用是全局平均池化层。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9bf2a3f2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "__all__ = ['MobileNetV2', 'MobileNetV2Backbone', 'MobileNetV2Head', 'mobilenet_v2']\n",
    "\n",
    "def _make_divisible(v, divisor, min_value=None):\n",
    "    if min_value is None:\n",
    "        min_value = divisor\n",
    "    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n",
    "    if new_v < 0.9 * v:\n",
    "        new_v += divisor\n",
    "    return new_v\n",
    "\n",
    "class GlobalAvgPooling(nn.Cell):\n",
    "    \"\"\"\n",
    "    Global avg pooling definition.\n",
    "\n",
    "    Args:\n",
    "\n",
    "    Returns:\n",
    "        Tensor, output tensor.\n",
    "\n",
    "    Examples:\n",
    "        >>> GlobalAvgPooling()\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        super(GlobalAvgPooling, self).__init__()\n",
    "\n",
    "    def construct(self, x):\n",
    "        x = P.mean(x, (2, 3))\n",
    "        return x\n",
    "\n",
    "class ConvBNReLU(nn.Cell):\n",
    "    \"\"\"\n",
    "    Convolution/Depthwise fused with Batchnorm and ReLU block definition.\n",
    "\n",
    "    Args:\n",
    "        in_planes (int): Input channel.\n",
    "        out_planes (int): Output channel.\n",
    "        kernel_size (int): Input kernel size.\n",
    "        stride (int): Stride size for the first convolutional layer. Default: 1.\n",
    "        groups (int): channel group. Convolution is 1 while Depthiwse is input channel. Default: 1.\n",
    "\n",
    "    Returns:\n",
    "        Tensor, output tensor.\n",
    "\n",
    "    Examples:\n",
    "        >>> ConvBNReLU(16, 256, kernel_size=1, stride=1, groups=1)\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1):\n",
    "        super(ConvBNReLU, self).__init__()\n",
    "        padding = (kernel_size - 1) // 2\n",
    "        in_channels = in_planes\n",
    "        out_channels = out_planes\n",
    "        if groups == 1:\n",
    "            conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='pad', padding=padding)\n",
    "        else:\n",
    "            out_channels = in_planes\n",
    "            conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='pad',\n",
    "                             padding=padding, group=in_channels)\n",
    "\n",
    "        layers = [conv, nn.BatchNorm2d(out_planes), nn.ReLU6()]\n",
    "        self.features = nn.SequentialCell(layers)\n",
    "\n",
    "    def construct(self, x):\n",
    "        output = self.features(x)\n",
    "        return output\n",
    "\n",
    "class InvertedResidual(nn.Cell):\n",
    "    \"\"\"\n",
    "    Mobilenetv2 residual block definition.\n",
    "\n",
    "    Args:\n",
    "        inp (int): Input channel.\n",
    "        oup (int): Output channel.\n",
    "        stride (int): Stride size for the first convolutional layer. Default: 1.\n",
    "        expand_ratio (int): expand ration of input channel\n",
    "\n",
    "    Returns:\n",
    "        Tensor, output tensor.\n",
    "\n",
    "    Examples:\n",
    "        >>> ResidualBlock(3, 256, 1, 1)\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, inp, oup, stride, expand_ratio):\n",
    "        super(InvertedResidual, self).__init__()\n",
    "        assert stride in [1, 2]\n",
    "\n",
    "        hidden_dim = int(round(inp * expand_ratio))\n",
    "        self.use_res_connect = stride == 1 and inp == oup\n",
    "\n",
    "        layers = []\n",
    "        if expand_ratio != 1:\n",
    "            layers.append(ConvBNReLU(inp, hidden_dim, kernel_size=1))\n",
    "        layers.extend([\n",
    "            ConvBNReLU(hidden_dim, hidden_dim,\n",
    "                       stride=stride, groups=hidden_dim),\n",
    "            nn.Conv2d(hidden_dim, oup, kernel_size=1,\n",
    "                      stride=1, has_bias=False),\n",
    "            nn.BatchNorm2d(oup),\n",
    "        ])\n",
    "        self.conv = nn.SequentialCell(layers)\n",
    "        self.cast = P.Cast()\n",
    "\n",
    "    def construct(self, x):\n",
    "        identity = x\n",
    "        x = self.conv(x)\n",
    "        if self.use_res_connect:\n",
    "            return P.add(identity, x)\n",
    "        return x\n",
    "\n",
    "class MobileNetV2Backbone(nn.Cell):\n",
    "    \"\"\"\n",
    "    MobileNetV2 architecture.\n",
    "\n",
    "    Args:\n",
    "        class_num (int): number of classes.\n",
    "        width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.\n",
    "        has_dropout (bool): Is dropout used. Default is false\n",
    "        inverted_residual_setting (list): Inverted residual settings. Default is None\n",
    "        round_nearest (list): Channel round to . Default is 8\n",
    "    Returns:\n",
    "        Tensor, output tensor.\n",
    "\n",
    "    Examples:\n",
    "        >>> MobileNetV2(num_classes=1000)\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, width_mult=1., inverted_residual_setting=None, round_nearest=8,\n",
    "                 input_channel=32, last_channel=1280):\n",
    "        super(MobileNetV2Backbone, self).__init__()\n",
    "        block = InvertedResidual\n",
    "        # setting of inverted residual blocks\n",
    "        self.cfgs = inverted_residual_setting\n",
    "        if inverted_residual_setting is None:\n",
    "            self.cfgs = [\n",
    "                # t, c, n, s\n",
    "                [1, 16, 1, 1],\n",
    "                [6, 24, 2, 2],\n",
    "                [6, 32, 3, 2],\n",
    "                [6, 64, 4, 2],\n",
    "                [6, 96, 3, 1],\n",
    "                [6, 160, 3, 2],\n",
    "                [6, 320, 1, 1],\n",
    "            ]\n",
    "\n",
    "        # building first layer\n",
    "        input_channel = _make_divisible(input_channel * width_mult, round_nearest)\n",
    "        self.out_channels = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)\n",
    "        features = [ConvBNReLU(3, input_channel, stride=2)]\n",
    "        # building inverted residual blocks\n",
    "        for t, c, n, s in self.cfgs:\n",
    "            output_channel = _make_divisible(c * width_mult, round_nearest)\n",
    "            for i in range(n):\n",
    "                stride = s if i == 0 else 1\n",
    "                features.append(block(input_channel, output_channel, stride, expand_ratio=t))\n",
    "                input_channel = output_channel\n",
    "        features.append(ConvBNReLU(input_channel, self.out_channels, kernel_size=1))\n",
    "        self.features = nn.SequentialCell(features)\n",
    "        self._initialize_weights()\n",
    "\n",
    "    def construct(self, x):\n",
    "        x = self.features(x)\n",
    "        return x\n",
    "\n",
    "    def _initialize_weights(self):\n",
    "        \"\"\"\n",
    "        Initialize weights.\n",
    "\n",
    "        Args:\n",
    "\n",
    "        Returns:\n",
    "            None.\n",
    "\n",
    "        Examples:\n",
    "            >>> _initialize_weights()\n",
    "        \"\"\"\n",
    "        self.init_parameters_data()\n",
    "        for _, m in self.cells_and_names():\n",
    "            if isinstance(m, nn.Conv2d):\n",
    "                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n",
    "                m.weight.set_data(Tensor(np.random.normal(0, np.sqrt(2. / n),\n",
    "                                                          m.weight.data.shape).astype(\"float32\")))\n",
    "                if m.bias is not None:\n",
    "                    m.bias.set_data(\n",
    "                        Tensor(np.zeros(m.bias.data.shape, dtype=\"float32\")))\n",
    "            elif isinstance(m, nn.BatchNorm2d):\n",
    "                m.gamma.set_data(\n",
    "                    Tensor(np.ones(m.gamma.data.shape, dtype=\"float32\")))\n",
    "                m.beta.set_data(\n",
    "                    Tensor(np.zeros(m.beta.data.shape, dtype=\"float32\")))\n",
    "\n",
    "    @property\n",
    "    def get_features(self):\n",
    "        return self.features\n",
    "\n",
    "class MobileNetV2Head(nn.Cell):\n",
    "    \"\"\"\n",
    "    MobileNetV2 architecture.\n",
    "\n",
    "    Args:\n",
    "        class_num (int): Number of classes. Default is 1000.\n",
    "        has_dropout (bool): Is dropout used. Default is false\n",
    "    Returns:\n",
    "        Tensor, output tensor.\n",
    "\n",
    "    Examples:\n",
    "        >>> MobileNetV2(num_classes=1000)\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, input_channel=1280, num_classes=1000, has_dropout=False, activation=\"None\"):\n",
    "        super(MobileNetV2Head, self).__init__()\n",
    "        # mobilenet head\n",
    "        head = ([GlobalAvgPooling(), nn.Dense(input_channel, num_classes, has_bias=True)] if not has_dropout else\n",
    "                [GlobalAvgPooling(), nn.Dropout(0.2), nn.Dense(input_channel, num_classes, has_bias=True)])\n",
    "        self.head = nn.SequentialCell(head)\n",
    "        self.need_activation = True\n",
    "        if activation == \"Sigmoid\":\n",
    "            self.activation = nn.Sigmoid()\n",
    "        elif activation == \"Softmax\":\n",
    "            self.activation = nn.Softmax()\n",
    "        else:\n",
    "            self.need_activation = False\n",
    "        self._initialize_weights()\n",
    "\n",
    "    def construct(self, x):\n",
    "        x = self.head(x)\n",
    "        if self.need_activation:\n",
    "            x = self.activation(x)\n",
    "        return x\n",
    "\n",
    "    def _initialize_weights(self):\n",
    "        \"\"\"\n",
    "        Initialize weights.\n",
    "\n",
    "        Args:\n",
    "\n",
    "        Returns:\n",
    "            None.\n",
    "\n",
    "        Examples:\n",
    "            >>> _initialize_weights()\n",
    "        \"\"\"\n",
    "        self.init_parameters_data()\n",
    "        for _, m in self.cells_and_names():\n",
    "            if isinstance(m, nn.Dense):\n",
    "                m.weight.set_data(Tensor(np.random.normal(\n",
    "                    0, 0.01, m.weight.data.shape).astype(\"float32\")))\n",
    "                if m.bias is not None:\n",
    "                    m.bias.set_data(\n",
    "                        Tensor(np.zeros(m.bias.data.shape, dtype=\"float32\")))\n",
    "    @property\n",
    "    def get_head(self):\n",
    "        return self.head\n",
    "\n",
    "class MobileNetV2(nn.Cell):\n",
    "    \"\"\"\n",
    "    MobileNetV2 architecture.\n",
    "\n",
    "    Args:\n",
    "        class_num (int): number of classes.\n",
    "        width_mult (int): Channels multiplier for round to 8/16 and others. Default is 1.\n",
    "        has_dropout (bool): Is dropout used. Default is false\n",
    "        inverted_residual_setting (list): Inverted residual settings. Default is None\n",
    "        round_nearest (list): Channel round to . Default is 8\n",
    "    Returns:\n",
    "        Tensor, output tensor.\n",
    "\n",
    "    Examples:\n",
    "        >>> MobileNetV2(backbone, head)\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, num_classes=1000, width_mult=1., has_dropout=False, inverted_residual_setting=None, \\\n",
    "        round_nearest=8, input_channel=32, last_channel=1280):\n",
    "        super(MobileNetV2, self).__init__()\n",
    "        self.backbone = MobileNetV2Backbone(width_mult=width_mult, \\\n",
    "            inverted_residual_setting=inverted_residual_setting, \\\n",
    "            round_nearest=round_nearest, input_channel=input_channel, last_channel=last_channel).get_features\n",
    "        self.head = MobileNetV2Head(input_channel=self.backbone.out_channel, num_classes=num_classes, \\\n",
    "            has_dropout=has_dropout).get_head\n",
    "\n",
    "    def construct(self, x):\n",
    "        x = self.backbone(x)\n",
    "        x = self.head(x)\n",
    "        return x\n",
    "\n",
    "class MobileNetV2Combine(nn.Cell):\n",
    "    \"\"\"\n",
    "    MobileNetV2Combine architecture.\n",
    "\n",
    "    Args:\n",
    "        backbone (Cell): the features extract layers.\n",
    "        head (Cell):  the fully connected layers.\n",
    "    Returns:\n",
    "        Tensor, output tensor.\n",
    "\n",
    "    Examples:\n",
    "        >>> MobileNetV2(num_classes=1000)\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, backbone, head):\n",
    "        super(MobileNetV2Combine, self).__init__(auto_prefix=False)\n",
    "        self.backbone = backbone\n",
    "        self.head = head\n",
    "\n",
    "    def construct(self, x):\n",
    "        x = self.backbone(x)\n",
    "        x = self.head(x)\n",
    "        return x\n",
    "\n",
    "def mobilenet_v2(backbone, head):\n",
    "    return MobileNetV2Combine(backbone, head)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "580c5ec0",
   "metadata": {},
   "source": [
    "## 6、MobileNetV2模型的训练与测试"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "168ec5f3",
   "metadata": {},
   "source": [
    "###### 训练策略\n",
    "\n",
    "一般情况下，模型训练时采用静态学习率，如0.01。随着训练步数的增加，模型逐渐趋于收敛，对权重参数的更新幅度应该逐渐降低，以减小模型训练后期的抖动。所以，模型训练时可以采用动态下降的学习率，常见的学习率下降策略有：\n",
    "- polynomial decay/square decay;\n",
    "- cosine decay;\n",
    "- exponential decay;\n",
    "- stage decay.\n",
    "\n",
    "这里使用cosine decay下降策略："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1d2152dd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def cosine_decay(total_steps, lr_init=0.0, lr_end=0.0, lr_max=0.1, warmup_steps=0):\n",
    "    \"\"\"\n",
    "    Applies cosine decay to generate learning rate array.\n",
    "\n",
    "    Args:\n",
    "       total_steps(int): all steps in training.\n",
    "       lr_init(float): init learning rate.\n",
    "       lr_end(float): end learning rate\n",
    "       lr_max(float): max learning rate.\n",
    "       warmup_steps(int): all steps in warmup epochs.\n",
    "\n",
    "    Returns:\n",
    "       list, learning rate array.\n",
    "    \"\"\"\n",
    "    lr_init, lr_end, lr_max = float(lr_init), float(lr_end), float(lr_max)\n",
    "    decay_steps = total_steps - warmup_steps\n",
    "    lr_all_steps = []\n",
    "    inc_per_step = (lr_max - lr_init) / warmup_steps if warmup_steps else 0\n",
    "    for i in range(total_steps):\n",
    "        if i < warmup_steps:\n",
    "            lr = lr_init + inc_per_step * (i + 1)\n",
    "        else:\n",
    "            cosine_decay = 0.5 * (1 + math.cos(math.pi * (i - warmup_steps) / decay_steps))\n",
    "            lr = (lr_max - lr_end) * cosine_decay + lr_end\n",
    "        lr_all_steps.append(lr)\n",
    "\n",
    "    return lr_all_steps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dcae4cc",
   "metadata": {},
   "source": [
    "在模型训练过程中，可以添加检查点（Checkpoint）用于保存模型的参数，以便进行推理及中断后再训练使用。使用场景如下：\n",
    "\n",
    "- 训练后推理场景\n",
    "1) 模型训练完毕后保存模型的参数，用于推理或预测操作。\n",
    "2) 训练过程中，通过实时验证精度，把精度最高的模型参数保存下来，用于预测操作。\n",
    "- 再训练场景\n",
    "1) 进行长时间训练任务时，保存训练过程中的Checkpoint文件，防止任务异常退出后从初始状态开始训练。\n",
    "2) Fine-tuning（微调）场景，即训练一个模型并保存参数，基于该模型，面向第二个类似任务进行模型训练。\n",
    "\n",
    "这里加载ImageNet数据上预训练的MobileNetv2进行Fine-tuning，只训练最后修改的FC层，并在训练过程中保存Checkpoint。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b4038b69",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def switch_precision(net, data_type):\n",
    "    if ms.get_context('device_target') == \"Ascend\":\n",
    "        net.to_float(data_type)\n",
    "        for _, cell in net.cells_and_names():\n",
    "            if isinstance(cell, nn.Dense):\n",
    "                cell.to_float(ms.float32)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17648e20",
   "metadata": {},
   "source": [
    "###### 模型训练与测试\n",
    "\n",
    "在进行正式的训练之前，定义训练函数，读取数据并对模型进行实例化，定义优化器和损失函数。\n",
    "\n",
    "首先简单介绍损失函数及优化器的概念：\n",
    "\n",
    "- 损失函数：又叫目标函数，用于衡量预测值与实际值差异的程度。深度学习通过不停地迭代来缩小损失函数的值。定义一个好的损失函数，可以有效提高模型的性能。\n",
    "\n",
    "- 优化器：用于最小化损失函数，从而在训练过程中改进模型。\n",
    "\n",
    "定义了损失函数后，可以得到损失函数关于权重的梯度。梯度用于指示优化器优化权重的方向，以提高模型性能。\n",
    "\n",
    "在训练MobileNetV2之前对MobileNetV2Backbone层的参数进行了固定，使其在训练过程中对该模块的权重参数不进行更新；只对MobileNetV2Head模块的参数进行更新。\n",
    "\n",
    "MindSpore支持的损失函数有SoftmaxCrossEntropyWithLogits、L1Loss、MSELoss等。这里使用SoftmaxCrossEntropyWithLogits损失函数。\n",
    "\n",
    "训练测试过程中会打印loss值，loss值会波动，但总体来说loss值会逐步减小，精度逐步提高。每个人运行的loss值有一定随机性，不一定完全相同。\n",
    "\n",
    "每打印一个epoch后模型都会在测试集上的计算测试精度，从打印的精度值分析MobileNetV2模型的预测能力在不断提升。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba84cebe55bdd08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mindspore.amp import FixedLossScaleManager\n",
    "import time\n",
    "LOSS_SCALE = 1024\n",
    "\n",
    "train_dataset = create_dataset(dataset_path=config.dataset_path, config=config)\n",
    "eval_dataset = create_dataset(dataset_path=config.dataset_path, config=config)\n",
    "step_size = train_dataset.get_dataset_size()\n",
    "    \n",
    "backbone = MobileNetV2Backbone() #last_channel=config.backbone_out_channels\n",
    "# Freeze parameters of backbone. You can comment these two lines.\n",
    "for param in backbone.get_parameters():\n",
    "    param.requires_grad = False\n",
    "# load parameters from pretrained model\n",
    "load_checkpoint(config.pretrained_ckpt, backbone)\n",
    "\n",
    "head = MobileNetV2Head(input_channel=backbone.out_channels, num_classes=config.num_classes)\n",
    "network = mobilenet_v2(backbone, head)\n",
    "\n",
    "# define loss, optimizer, and model\n",
    "loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')\n",
    "loss_scale = FixedLossScaleManager(LOSS_SCALE, drop_overflow_update=False)\n",
    "lrs = cosine_decay(config.epochs * step_size, lr_max=config.lr_max)\n",
    "opt = nn.Momentum(network.trainable_params(), lrs, config.momentum, config.weight_decay, loss_scale=LOSS_SCALE)\n",
    "\n",
    "# 定义用于训练的train_loop函数。\n",
    "def train_loop(model, dataset, loss_fn, optimizer):\n",
    "    # 定义正向计算函数\n",
    "    def forward_fn(data, label):\n",
    "        logits = model(data)\n",
    "        loss = loss_fn(logits, label)\n",
    "        return loss\n",
    "\n",
    "    # 定义微分函数，使用mindspore.value_and_grad获得微分函数grad_fn,输出loss和梯度。\n",
    "    # 由于是对模型参数求导,grad_position 配置为None，传入可训练参数。\n",
    "    grad_fn = ms.value_and_grad(forward_fn, None, optimizer.parameters)\n",
    "\n",
    "    # 定义 one-step training函数\n",
    "    def train_step(data, label):\n",
    "        loss, grads = grad_fn(data, label)\n",
    "        optimizer(grads)\n",
    "        return loss\n",
    "\n",
    "    size = dataset.get_dataset_size()\n",
    "    model.set_train()\n",
    "    for batch, (data, label) in enumerate(dataset.create_tuple_iterator()):\n",
    "        loss = train_step(data, label)\n",
    "\n",
    "        if batch % 10 == 0:\n",
    "            loss, current = loss.asnumpy(), batch\n",
    "            print(f\"loss: {loss:>7f}  [{current:>3d}/{size:>3d}]\")\n",
    "\n",
    "# 定义用于测试的test_loop函数。\n",
    "def test_loop(model, dataset, loss_fn):\n",
    "    num_batches = dataset.get_dataset_size()\n",
    "    model.set_train(False)\n",
    "    total, test_loss, correct = 0, 0, 0\n",
    "    for data, label in dataset.create_tuple_iterator():\n",
    "        pred = model(data)\n",
    "        total += len(data)\n",
    "        test_loss += loss_fn(pred, label).asnumpy()\n",
    "        correct += (pred.argmax(1) == label).asnumpy().sum()\n",
    "    test_loss /= num_batches\n",
    "    correct /= total\n",
    "    print(f\"Test: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")\n",
    "\n",
    "print(\"============== Starting Training ==============\")\n",
    "# 由于时间问题，训练过程只进行了2个epoch ，可以根据需求调整。\n",
    "epoch_begin_time = time.time()\n",
    "epochs = 2\n",
    "for t in range(epochs):\n",
    "    begin_time = time.time()\n",
    "    print(f\"Epoch {t+1}\\n-------------------------------\")\n",
    "    train_loop(network, train_dataset, loss, opt)\n",
    "    ms.save_checkpoint(network, \"save_mobilenetV2_model.ckpt\")\n",
    "    end_time = time.time()\n",
    "    times = end_time - begin_time\n",
    "    print(f\"per epoch time: {times}s\")\n",
    "    test_loop(network, eval_dataset, loss)\n",
    "epoch_end_time = time.time()\n",
    "times = epoch_end_time - epoch_begin_time\n",
    "print(f\"total time:  {times}s\")\n",
    "print(\"============== Training Success ==============\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cfecd53",
   "metadata": {},
   "source": [
    "## 7、模型推理\n",
    "\n",
    "加载模型Checkpoint进行推理，使用load_checkpoint接口加载数据时，需要把数据传入给原始网络，而不能传递给带有优化器和损失函数的训练网络。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "02243925",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "CKPT=\"save_mobilenetV2_model.ckpt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3785d4f3655528fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_process(image):\n",
    "    \"\"\"Precess one image per time.\n",
    "    \n",
    "    Args:\n",
    "        image: shape (H, W, C)\n",
    "    \"\"\"\n",
    "    mean=[0.485*255, 0.456*255, 0.406*255]\n",
    "    std=[0.229*255, 0.224*255, 0.225*255]\n",
    "    image = (np.array(image) - mean) / std\n",
    "    image = image.transpose((2,0,1))\n",
    "    img_tensor = Tensor(np.array([image], np.float32))\n",
    "    return img_tensor\n",
    "\n",
    "def infer_one(network, image_path):\n",
    "    image = Image.open(image_path).resize((config.image_height, config.image_width))\n",
    "    logits = network(image_process(image))\n",
    "    pred = np.argmax(logits.asnumpy(), axis=1)[0]\n",
    "    print(image_path, class_en[pred])\n",
    "\n",
    "def infer():\n",
    "    backbone = MobileNetV2Backbone(last_channel=config.backbone_out_channels)\n",
    "    head = MobileNetV2Head(input_channel=backbone.out_channels, num_classes=config.num_classes)\n",
    "    network = mobilenet_v2(backbone, head)\n",
    "    load_checkpoint(CKPT, network)\n",
    "    for i in range(91, 100):\n",
    "        infer_one(network, f'data_en/test/Cardboard/000{i}.jpg')\n",
    "infer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885ca673",
   "metadata": {},
   "source": [
    "## 8、导出AIR/GEIR/ONNX模型文件\n",
    "\n",
    "导出AIR模型文件，用于后续Atlas 200 DK上的模型转换与推理。当前仅支持MindSpore+Ascend环境。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ed6f328f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "backbone = MobileNetV2Backbone(last_channel=config.backbone_out_channels)\n",
    "head = MobileNetV2Head(input_channel=backbone.out_channels, num_classes=config.num_classes)\n",
    "network = mobilenet_v2(backbone, head)\n",
    "load_checkpoint(CKPT, network)\n",
    "\n",
    "input = np.random.uniform(0.0, 1.0, size=[1, 3, 224, 224]).astype(np.float32)\n",
    "# export(network, Tensor(input), file_name='mobilenetv2.air', file_format='AIR')\n",
    "# export(network, Tensor(input), file_name='mobilenetv2.pb', file_format='GEIR')\n",
    "export(network, Tensor(input), file_name='mobilenetv2.onnx', file_format='ONNX')"
   ]
  }
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